def run()

in python/singa/data.py [0:0]

• 47 lines of code
• 14 McCabe index (conditional complexity)

    def run(self):
        img_list = []
        is_label_index = True
        for line in open(self.img_list_file, 'r'):
            item = line.strip('\n').split(self.delimiter)
            if len(item) < 2:
                is_label_index = False
                img_list.append((item[0].strip(), None))
            else:
                if not item[1].strip().isdigit():
                    # the meta info is not label index
                    is_label_index = False
                img_list.append((item[0].strip(), item[1].strip()))
        index = 0  # index for the image
        if self.shuffle:
            random.shuffle(img_list)
        while not self.stop:
            if not self.queue.full():
                x, y = [], []
                i = 0
                while i < self.batch_size:
                    img_path, img_meta = img_list[index]
                    aug_images = self.image_transform(
                        os.path.join(self.image_folder, img_path))
                    assert i + len(aug_images) <= self.batch_size, \
                        'too many images (%d) in a batch (%d)' % \
                        (i + len(aug_images), self.batch_size)
                    for img in aug_images:
                        ary = np.asarray(img.convert('RGB'), dtype=np.float32)
                        x.append(ary.transpose(2, 0, 1))
                        if is_label_index:
                            y.append(int(img_meta))
                        else:
                            y.append(img_meta)
                        i += 1
                    index += 1
                    if index == self.num_samples:
                        index = 0  # reset to the first image
                        if self.shuffle:
                            random.shuffle(img_list)
                # enqueue one mini-batch
                if is_label_index:
                    self.queue.put((np.asarray(x), np.asarray(y,
                                                              dtype=np.int32)))
                else:
                    self.queue.put((np.asarray(x), y))
            else:
                time.sleep(0.1)
        return